Description
Book SynopsisSince the publication of the first edition of this book, the area of mathematical finance has grown rapidly, with financial analysts using more sophisticated mathematical concepts, such as stochastic integration, to describe the behavior of markets and to derive computing methods. Maintaining the lucid style of its popular predecessor, Introduction to Stochastic Calculus Applied to Finance, Second Edition incorporates some of these new techniques and concepts to provide an accessible, up-to-date initiation to the field.
New to the Second Edition
Complements on discrete models, including Rogers'' approach to the fundamental theorem of asset pricing and super-replication in incomplete markets
Discussions on local volatility, Dupire''s formula, the change of numéraire techniques, forward measures, and the forward Libor model
A new chapter on credit risk modeling
An extension of the chapter on simul
Trade Review
The second edition of this book provides a concise and accessible introduction to the probabilistic techniques needed to understand the most widely used financial models. This edition incorporates many new techniques and concepts to be used to describe the behavior of financial markets. … the solutions obtained using SciLab for computer experiments are available at http://cermics.enpc.fr/~bl/scilab/ These experiments were well designed by the authors based on their teaching and research experience and were found to be effective in communicating these concepts and ideas and enhancing the understanding of readers. … a solid introduction to stochastic approaches used in the financial world. The authors cover many key finance topics … . The book can be used as a reference text by researchers and graduate students in financial mathematics. It also is ideal reading material for practicing financial analysts and consultants using mathematical models for finance.
—Technometrics, May 2009, Vol. 51, No. 2
Table of Contents
Discrete-Time Models. Optimal Stopping Problem and American Options. Brownian Motion and Stochastic Differential Equations. The Black-Scholes Model. Option Pricing and Partial Differential Equations. Interest Rate Models. Asset Models with Jumps. Credit Risk Models. Simulation and Algorithms for Financial Models. Appendix. Bibliography. Index.
